



import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase_										=  16
lowercase_										=  32



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_ = 16    ,	SCREAMING_SNAKE_CASE_ = "bert-base-cased"	):
			lowercase__       	=					AutoTokenizer.from_pretrained(a__	)
			lowercase__       	=					load_dataset("glue"    ,	"mrpc"	)

			def tokenize_function(SCREAMING_SNAKE_CASE_	):
						# max_length=None => use the model max length (it's actually the default)
						lowercase__       	=					tokenizer(examples["sentence1"]    ,	examples["sentence2"]    ,	truncation=a__    ,	max_length=a__	)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			lowercase__       	=					datasets.map(
			    a__    ,	batched=a__    ,	remove_columns=["idx", "sentence1", "sentence2"]    ,	load_from_cache_file=a__	)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			lowercase__       	=					tokenized_datasets.rename_column("label"    ,	"labels"	)

			def collate_fn(SCREAMING_SNAKE_CASE_	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(a__    ,	padding="max_length"    ,	max_length=128    ,	return_tensors="pt"	)
						return tokenizer.pad(a__    ,	padding="longest"    ,	return_tensors="pt"	)

			# Instantiate dataloaders.
			lowercase__       	=					DataLoader(
			    tokenized_datasets["train"]    ,	shuffle=a__    ,	collate_fn=a__    ,	batch_size=a__	)
			lowercase__       	=					DataLoader(
			    tokenized_datasets["validation"]    ,	shuffle=a__    ,	collate_fn=a__    ,	batch_size=a__	)

			return train_dataloader, eval_dataloader



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowercase__       	=					config["lr"]
			lowercase__       	=					int(config["num_epochs"]	)
			lowercase__       	=					int(config["seed"]	)
			lowercase__       	=					int(config["batch_size"]	)
			lowercase__       	=					args.model_name_or_path

			set_seed(a__	)
			lowercase__	,							lowercase__       	=					get_dataloaders(a__    ,	a__    ,	a__	)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowercase__       	=					AutoModelForSequenceClassification.from_pretrained(a__    ,	return_dict=a__	)

			# Instantiate optimizer
			lowercase__       	=					(
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			lowercase__       	=					optimizer_cls(params=model.parameters()    ,	lr=a__	)

			if accelerator.state.deepspeed_plugin is not None:
						lowercase__       	=					accelerator.state.deepspeed_plugin.deepspeed_config[
						    "gradient_accumulation_steps"
						]
			else:
						lowercase__       	=					1
			lowercase__       	=					(len(a__	) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						lowercase__       	=					get_linear_schedule_with_warmup(
						    optimizer=a__    ,	num_warmup_steps=0    ,	num_training_steps=a__    ,	)
			else:
						lowercase__       	=					DummyScheduler(a__    ,	total_num_steps=a__    ,	warmup_num_steps=0	)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			lowercase__	,							lowercase__	,							lowercase__	,							lowercase__	,							lowercase__       	=					accelerator.prepare(
			    a__    ,	a__    ,	a__    ,	a__    ,	a__	)

			# We need to keep track of how many total steps we have iterated over
			lowercase__       	=					0
			# We also need to keep track of the stating epoch so files are named properly
			lowercase__       	=					0

			# Now we train the model
			lowercase__       	=					evaluate.load("glue"    ,	"mrpc"	)
			lowercase__       	=					0
			lowercase__       	=					{}
			for epoch in range(a__    ,	a__	):
						model.train()
						for step, batch in enumerate(a__	):
									lowercase__       	=					model(**a__	)
									lowercase__       	=					outputs.loss
									lowercase__       	=					loss / gradient_accumulation_steps
									accelerator.backward(a__	)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						lowercase__       	=					0
						for step, batch in enumerate(a__	):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device	)
									with torch.no_grad():
												lowercase__       	=					model(**a__	)
									lowercase__       	=					outputs.logits.argmax(dim=-1	)
									# It is slightly faster to call this once, than multiple times
									lowercase__	,							lowercase__       	=					accelerator.gather(
									    (predictions, batch["labels"])	)  # If we are in a multiprocess environment, the last batch has duplicates
									if accelerator.use_distributed:
												if step == len(a__	) - 1:
															lowercase__       	=					predictions[: len(eval_dataloader.dataset	) - samples_seen]
															lowercase__       	=					references[: len(eval_dataloader.dataset	) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=a__    ,	references=a__    ,	)

						lowercase__       	=					metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:'''    ,	a__	)
						lowercase__       	=					eval_metric["accuracy"]

						if best_performance < eval_metric["accuracy"]:
									lowercase__       	=					eval_metric["accuracy"]

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir    ,	"all_results.json"	)    ,	"w"	) as f:
									json.dump(a__    ,	a__	)



def __lowerCAmelCase			(  ):
			lowercase__       	=					argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."	)
			parser.add_argument(
			    "--model_name_or_path"    ,	type=a__    ,	default="bert-base-cased"    ,	help="Path to pretrained model or model identifier from huggingface.co/models."    ,	required=a__    ,	)
			parser.add_argument(
			    "--output_dir"    ,	type=a__    ,	default="."    ,	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,	)
			parser.add_argument(
			    "--performance_lower_bound"    ,	type=a__    ,	default=a__    ,	help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."    ,	)
			parser.add_argument(
			    "--num_epochs"    ,	type=a__    ,	default=3    ,	help="Number of train epochs."    ,	)
			lowercase__       	=					parser.parse_args()
			lowercase__       	=					{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
			training_function(a__    ,	a__	)


if __name__ == "__main__":
							main()





from __future__ import annotations

from collections import Counter
from random import random




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   List[str])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   str)						->	None:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   str							, lowercase_				:   str							, lowercase_				:   float)						->	None:


   """simple docstring"""







   if nodea not in self.connections:
     self.add_node(lowercase_)
   if nodea not in self.connections:
     self.add_node(lowercase_)
   _UpperCamelCase           =    probability







 def 		__UpperCAmelCase				(		self				:   Any)						->	list[str]:


   """simple docstring"""







   return list(self.connections)







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   str)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    0
   _UpperCamelCase           =    random()

   for dest in self.connections[node]:
     current_probability += self.connections[node][dest]
     if current_probability > random_value:
       return dest
   return ""


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->dict[str, int]:




  '''simple docstring'''

  _UpperCamelCase           =    MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(a__    ,     a__    ,     a__ )

  _UpperCamelCase           =    Counter(graph.get_nodes() )
  _UpperCamelCase           =    start

  for _ in range(a__ ):
    _UpperCamelCase           =    graph.transition(a__ )
    visited[node] += 1

  return visited


if __name__ == "__main__":
   import doctest

   doctest.testmod()




